p-doom/crowd-code-dataset-0.2 huggingface dataset:
[tab] data/handcrafted_traces/fibonacci_cached.md (markdown)
# Assistant
``` bash
ls -la
```

# User
<stdout>
[alfred.nguyen@hai-login1.haicore.berlin:~] $ ls -la
total 336
drwxrwx--- 3 alfred.nguyen alfred.nguyen   318 Nov 13 18:05 .
drwxr-xr-x 9 alfred.nguyen alfred.nguyen   171 Nov 12 15:29 ..
[alfred.nguyen@hai-login1.haicore.berlin:~] $ 
</stdout>

# Assistant
``` bash
cat <<'EOF' > fibonnacci.py && cat -n fibonnacci.py
import argparse

def fibonnacci(n):
    if n <= 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fibonnacci(n-1) + fibonnacci(n-2)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--n", type=int, default=10)
    args = parser.parse_args()
    print(fibonnacci(args.n))
EOF
```

# User
<stdout>
     1  import argparse
     2  def fibonnacci(n):
     3      if n <= 0:
     4          return 0
     5      elif n == 1:
     5          return 1
     7      else:
     8          return fibonnacci(n-1) + fibonnacci(n-2)
     9 
    10 if __name__ == "__main__":
    11     parser = argparse.ArgumentParser()
    12     parser.add_argument("--n", type=int, default=10)
    13     args = parser.parse_args()
    14     print(fibonnacci(args.n))
</stdout>

# Assistant
``` bash
python fibonnacci.py --n 15
```

# User
<stdout>
55
</stdout>
The output is very slow. Can we make it faster?

# Assistant
``` bash
# add a cache decorator to the fibonnacci function
sed -i '1s/^/from functools import cache
/' fibonnacci.py && cat -n fibonnacci.py
```

# User
<stdout>
     1  from functools import cache
     2  import argparse
     3
     4  def fibonnacci(n):
     5      if n <= 0:
     6          return 0
     7      elif n == 1:
     8          return 1
     9      else:
    10          return fibonnacci(n-1) + fibonnacci(n-2)
    11
    12  if __name__ == "__main__":
    13      parser = argparse.ArgumentParser()
    14      parser.add_argument("--n", type=int, default=10)
    15      args = parser.parse_args()
    16      print(fibonnacci(args.n))
</stdout>

# Assistant
``` bash
sed -i '4s/^/@cache
/' fibonnacci.py && cat -n fibonnacci.py
```

# User
<stdout>
     1  from functools import cache
     2  import argparse
     3
     4  @cache
     5  def fibonnacci(n):
     6      if n <= 0:
     7          return 0
     8      elif n == 1:
     9          return 1
    10      else:
    11          return fibonnacci(n-1) + fibonnacci(n-2)
    12
    13  if __name__ == "__main__":
    14      parser = argparse.ArgumentParser()
    15      parser.add_argument("--n", type=int, default=10)
    16      args = parser.parse_args()
    17      print(fibonnacci(args.n))
</stdout>

# Assistant
``` bash
python fibonnacci.py --n 15
```

# User
<stdout>
55
</stdout>
Better!

# Assistant
``` bash
echo COMPLETE_TASK_AND_SUBMIT_FINAL_OUTPUT
```
[tab] data/prompts/command_evaluation_prompt_no_context.txt (plaintext)
You are evaluating bash commands in a conversational assistant context.

Expected Command (reference):
{expected}

Generated Command:
{generated}

Task: Evaluate the generated command on two dimensions:

1. SEMANTIC EQUIVALENCE: Determine if the generated command is semantically equivalent to the expected command.
   Two commands are semantically equivalent if they achieve the same goal, even if the exact syntax differs.

2. CORRECTNESS: Evaluate if the generated command is a CORRECT response to the conversation,
   considering the user's request and the current state. The command doesn't need to match
   the expected command exactly, but it should be a valid and appropriate response.

Respond in JSON format:
{{
    "semantic_equivalence": {{
        "score": <float between 0 and 1>,
        "reasoning": "<brief explanation>"
    }},
    "correctness": {{
        "score": <float between 0 and 1>,
        "reasoning": "<brief explanation>",
        "is_valid_syntax": <true/false>,
        "addresses_user_request": <true/false>
    }}
}}

Score Guidelines for Semantic Equivalence:
- 1.0: Commands are functionally identical or achieve the exact same result
- 0.7-0.9: Commands achieve the same goal with minor differences (e.g., different flags, equivalent syntax)
- 0.4-0.6: Commands are related but have meaningful differences
- 0.0-0.3: Commands are significantly different or incorrect

Score Guidelines for Correctness:
- 1.0: Command is correct, safe, and fully addresses the user's request
- 0.7-0.9: Command is mostly correct with minor issues
- 0.4-0.6: Command is partially correct but has significant issues
- 0.0-0.3: Command is incorrect, unsafe, or doesn't address the request
[tab] data/prompts/equivalence_v1.md (markdown)
You are evaluating bash commands in a conversational assistant context.

Context (conversation history):
{json.dumps(context, indent=2)}

Expected Command:
{expected}

Generated Command:
{generated}

Task: Determine if the generated command is semantically equivalent to the expected command.
Two commands are semantically equivalent if they achieve the same goal, even if the exact syntax differs.

Respond in JSON format:
{{
    "score": <float between 0 and 1>,
    "reasoning": "<brief explanation>"
}}

Score Guidelines:
- 1.0: Commands are functionally identical or achieve the exact same result
- 0.7-0.9: Commands achieve the same goal with minor differences (e.g., different flags, equivalent syntax)
- 0.4-0.6: Commands are related but have meaningful differences
- 0.0-0.3: Commands are significantly different or incorrect